System and method for determining an authority rank for real time searching

ABSTRACT

The present invention is directed towards a method and system for processing a real time increase in search requests for a common event. The method and system includes detecting an activity spike in user search request activity based on monitoring of user search requests over a defined period of time and determining source locations associated with the activity spike based on user search result activities. The method and system further includes associating the source locations with the user search request and thereupon applying a machine-learning model to determine a plurality of common features operative to cause the activity spike, including determining associations between the source locations and the activity spike.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/706,349 filed Feb. 16, 2010, which is incorporated herein byreference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material,which is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF THE INVENTION

The invention described herein generally relates to search engines andmore specifically to systems and methods for processing and improvingsearch results for current real-time trends and/or events.

BACKGROUND OF THE INVENTION

Traditional search engines deal with multiple sets of informationcorpora. In response to a search request, the search engine returnsresult sets in an ordered listing. The reliability of search resultsoften depends on various factors, including the collection of theinformation, processing of the information, the information source anduser feedback on the veracity of this information.

Problems can arise when there is a spike in activity for a particularsearch trend because of problems with not only determining the rightcontemporaneous information, but also the reliability of thisinformation. Standard search terms can be easily and readily handledusing existing search technology, for example a user conducting a searchto find information on a vacation to Las Vegas.

But a spike in activity typically represents a corresponding real worldoccurrence and users seeking information as it becomes available. Forexample, suppose a natural disaster occurs or a rumor emerges that acompany is about to launch a ground-breaking new product, there will bea corresponding in spike in people searching for this information.

Current web searching technology suffers from an ability to successfullyaccount for contemporaneous information. There is a growing trend forhighly contemporaneous information achieving a critical mass ofdistribution in a very short time frame. This increase incontemporaneous information is predicated on the wide use and quickdissemination of information occurring in the current electronic world.

The conversion of the Internet from a passive online informationalsource to a de facto medium for information distribution, combined withthe new tools for increases contemporaneous content generation,complicates existing web searching technology. Examples ofcontemporaneous information may include data feeds, such as social mediafeeds, really simple syndication (RSS) feeds, web logs, etc. Priortechniques of crawling the Internet, cataloging and then searching thesecorpora suffer from a lack of proper accounting for thesecontemporaneous data sources.

With developments in search engine technology to account for thesefeeds, problems can arise in the reliability of this information. Forexample, just because a search engine may describe a social media feedthat includes information relating to the event, there is no way totrust the source of this feed. Therefore, there exists a need forimproving search results correlating to spikes in real time searchactivities by accounting for the authority of sources in the searchresult.

SUMMARY OF THE INVENTION

The present invention is directed towards a method and system forprocessing a real time increase in search requests for a common event.The method and system includes detecting an activity spike in usersearch request activity based on monitoring of user search requests overa defined period of time and determining source locations associatedwith the activity spike based on user search result activities. Themethod and system further includes associating the source locations withthe user search request and thereupon applying a machine-learning modelto determine a plurality of common features operative to cause theactivity spike, including determining associations between the sourcelocations and the activity spike.

The present invention further includes determining a plurality of freshweb content for a search engine and measuring a real-time authority forthe fresh content using the machine-learning model. Therein, the methodand system includes adjusting a reliability factor for the fresh webcontent based on the measured real-time authority. In one embodiment,the method and system, the adjustment of the reliability factor based onthe measured real-time authority is performed instead of at least one ofa link flux calculation and a page rank adjustment.

The present invention further includes determining the search terms ofthe user search requests associated with the activity spike anddetermining a plurality of additional content sources associated withthe search terms. The system and method further includes ranking theplurality of additional content sources based on the measured real-timeauthority. The system and method additionally includes determining anadditional processing capacity in the search engine caused by theactivity spike and allocating available processing capacity of thesearch engine proportional to the additional processing capacity.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated in the figures of the accompanying drawingswhich are meant to be exemplary and not limiting, in which likereferences are intended to refer to like or corresponding parts, and inwhich:

FIG. 1 illustrates one embodiment of a block diagram of a search resultsystem that includes capabilities of providing search results forcontemporaneous information;

FIG. 2 illustrates one embodiment of a search engine includingcapabilities for processing contemporaneous information from real timesources and determining an authority rank for the real-time sources;

FIG. 3 illustrates a flowchart of the steps of one embodiment of amethod for determining an authority rank for real time sources; and

FIG. 4 illustrates a flowchart of the steps of another embodiment of amethod for determining an authority rank for real time sources.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following description of the embodiments of the invention,reference is made to the accompanying drawings that form a part hereof,and in which is shown by way of illustration, exemplary embodiments inwhich the invention may be practiced. It is to be understood that otherembodiments may be utilized and structural changes may be made withoutdeparting from the scope of the present invention.

As described herein, the search engine technology recognizes a spike ordramatic increase in user search activity for a particular event ortheme. From that spike, the search engine is able to process searchresult options relating to real time sources. From the processing ofthose sources, the search engine therefore generates an authority rankfor the real time sources. Using this authority rank improves theordering of the search results.

FIG. 1 illustrates one embodiment of a system 100 that includes a userdevice 102, a network 104 and a search engine 106. The system 100further includes a database population module 108 and a content database110 usable by the search engine 106. In the system 100, additionalcontent sources 120 a, 120 b and 120 c are additionally connected viathe network 104.

The user device 102 may be any suitable type of user processing deviceas recognized by one skilled in the art. In a typical embodiment, theuser device 102 is a personal or mobile computing device that includeslocal processing capabilities, as well networking capabilities tointeract and engage the network 104.

The network 104 may be any suitable type of network allowing datacommunication thereacross. In a typical embodiment, the network 104 isthe Internet, following known Internet protocols for data communicationthereacross.

The search engine 106 is one or more processing components disposed onone or more processing devices or systems in a networked environment.The search engine 106 may operate similar to known search enginetechnologies, but with the inclusion of additional processingcapabilities describes herein. The search engine is operative to receivesearch requests and process the requests to generate search results tothe user device 102 across the network 104. Whereas, the search engine106 is additionally capable of recognizing a spike in search activity,recognizing contemporaneous sources of information, processing variousdetails of information and thereby ranking the sources.

It is recognized that various details of the user device 102, network104 and search engine 106 have been omitted. Many details, such astechniques for engaging and communication therebetween, not describedherein are known within the knowledge of one skilled in the art and areomitted for brevity purposes only.

The database population module 108 is illustrated as being separate fromthe search engine 106, but it is recognized that this module may beincorporated therein. The database population module 108 is a processingdevice or system operative to perform processing operations in responseto executable instructions, instructions for extracting searchinformation relating to web-based content and then populating thecontent database 110. In one embodiment, the module 108 may includetechnology crawling Internet content to populate the database 110.Additionally, the module 108 includes processing operations fordetermining contemporaneous sources for real time information.

The system 100 illustrates three sample contemporaneous sources 120 a,120 b and 120 c (collectively referred to as 120). The sources 120 canbe any type of source that provides real-time information. A typicalexample may be a social network feed. For example, a Twitter® feed fromvarious account users can be a real-time source. This real-time datafeed provides large amounts of contemporaneous information, withsignificant uncertainty regarding the veracity of this information.Another source could be a really simple syndication (RSS) feed or othertype of news or data feed, e.g. a stock ticker feed.

It is recognized that there are other types of information sources thatprovide real-time content and the sources 120 are not limited by theexamples listed above. As the speed of information is received, there isthe uncertainty of the trustworthiness of this information.

In the system 100, the user may enter a search request to the searchengine 106 via the network 104. The search engine accesses the database110 to find content results that answer the search inquiry, where basedon the population module 108, the database 110 includes real-timeinformation from the contemporaneous sources 120.

Search results are provided back to the user device 102. The ranking ofthese results are affected by a recognition in spike in user searchactivity and generating an authority rank. FIG. 2 describes in furtherdetail one embodiment of processing operations and subsequentmethodologies for processing the real time increase in search requestsand providing improved search results to the user device 102.

FIG. 2 illustrates a system 140 including an interface 142, search termmonitor 144, search processing engine 146, a source locator, 148, aspike content database 150 and a machine learning processing device 152.The system 140 may be embedded within or part of a larger search engine,such as the search engine 106 of FIG. 1, for example.

The interface 142 represents the computer executable code that providesthe front-end user experience for search operations, such as the userentering search terms and receiving search results in response thereto.The search term monitor 144 may be a processing device or module thatmonitors search terms over periods of time to determine if there is aparticular trend or a spike in activity. For example, a spike inactivity may be determined by specific standards, such as if there are Xnumber of search requests to the same common theme within Y seconds.Merely by way of example, a spike may including noting there are inexcess of 10,000 searches for the same or common terms within a periodof 30 seconds.

The search processing engine 146 is a processing device operative toprocess various aspects of the search engine operations. The engine 146may include receipt of the search term, accessing a database of searchresults and then generating the search results page in response thereto.The engine 146 includes additional processing capabilities for real-timerank authority as described in further detail below.

The source locator 148 may be a processing device or a module ofexecutable instructions, operative to perform operations relating todetermining a content source and allowing for various processingoperations relating to that source. As described in further detailbelow, it is important to find various information sources, typicallycontemporaneous sources, in real-time event search response scenarios.And when those sources are discovered, the system 140 is operative tothereby rank the sources to quantify the reliability of these sources,typically illustrated via the search result rankings.

The spike content database 150 is any suitable type of data storagedevice that stores spike information. This database 150 may include thestorage of search query information, e.g. search query terms, searchquery rewrites, search result actions, etc. This information is thenusable for tracking search query information over a period of time, asdescribed in further detail below.

In this system 140, the machine learning processing device 152 is one ormore processing devices operative to generate authority ranks The device152 uses machine learning operations to evaluate the authority of thedetermined sources, where in one embodiment the device 152 may use knownmachine learning techniques for ranking source authority, techniquesused in existing search engines for evaluating sources with existingcrawling techniques to crawl web content. Whereas, in the present system140, the timeliness of the real time authority rank complicates themachine learning process, such that the machine learning is modified tobe performed in a more expedited manner.

For further illustration of the systems of FIGS. 1 and 2, the operationsof these systems are described in further detail regarding themethodologies of FIGS. 3 and 4.

FIG. 3 illustrates a flowchart of one embodiment of a method processinga real time increase in search requests for a common event. The methodbegins, step 160, by detecting an activity spike in user search requestactivity based on monitoring of user search requests over a definedperiod of time.

As described above, in a typical search engine, various amounts ofsearches are conducted on a regular basis. There are larger trendsrelating to common events, such as for example there may be an increasein searches for an actor or actress around the time a movie premieres,or general searches to a sporting event around the time the sportingevent occurs.

By contrast, a spike in activity relates to an immediate jump insearching for information as may be caused by an immediate, typicallyunplanned event. Simply by way of example, an unplanned event may be anatural disaster, e.g. an earthquake in Haiti. The search engine 106,using the search term monitoring processing device 144, is thereforeable to determine an activity spike by detecting that over a very shortperiod of time there is an increase in the number of common searches.Using the example of a Haitian earthquake, the activity spike may berecognized as tens of thousands of searches for same or common terms,such as “Earthquake” and “Haiti.”

The period of time can readily be adjusted to determine differencesbetween a trend and an activity spike. A trend is more likely over anextended period of time, whereas an activity spike occurs in a truncatedtime period, whether it be seconds, minutes, hours, etc.

In the method of FIG. 3, a next step, step 162, is determining sourcelocations associated with the activity spike based on user search resultactivities. The source locations for real-time activities includecontemporaneous sources. Examples of contemporaneous sources may be anysource that includes information in real time or in a timely fashionlikely to be missed by web crawling techniques, for example a socialnetwork feed.

In this step, one example may be an event of a rumor of a high-techproduct launch. A source location may be a technology blog dedicated totracking and reporting on high tech rumors and news releases. Anothersource location may be a technical journal reporting on the blogarticle. This step may include determining that this web log and thejournal articles are the sources of the activity spike. In the exampleof a natural disaster, the source of the activity spike may be a newsweb location reporting on the event in a breaking news fashion. Othersources could be social network feeds, for example, from individuals atthe specification location.

A next step, step 164, is associating the source locations with the usersearch requests. Reference back to FIG. 2, the source locations 148 maydetermine these sources and the locations are stored in the spikecontent database 150. The locations can be stored and referenced by theassociated search request and/or search terms. For example, in theexample of a product launch, the web log and journal article may becataloged in the spike content database 150 referenced by the searchterms used in the spike of activity.

With a database cataloging the information, the machine learningprocessing device 152 is operative to perform the next step, step 166,of FIG. 3. The step includes applying a machine-learning model todetermine a plurality of common features operative to cause the activityspike including determining associations between the source locationsand the activity spike. It is recognized that there are any number ofsuitable machine-learning techniques operative to process theassociations described herein, including in one embodimentmachine-learning techniques applicable to web-crawling techniques innon-real time data processing and cataloging operations.

In step 168, the method further includes measuring a real-time authorityrank for search result items based on the machine learning models. Basedon this real time machine learning, the real-time authority indicates anauthority ranking determinative of the veracity of the source. Using theabove example of a rumored product launch, the web log may be given ahigh authority ranking based on the machine-learning factors indicatingit is a highly trustworthy source.

By contrast, it is also possible that another source could be asecondary, less reliable web log indicating the product rumor. This lessreliable web log may be less reliable for any number of reasons, such asit regularly broadcasts various rumors, is associated with a competingbusiness, is associated with an illegal stock manipulation scheme, justby way of example. Using the machine learning operations of the machinelearning processing device 152, this particular web location is thengiven a low authority for search results.

Based on determination of various sources, machine-learning processingand generating authority ranks, the search processing engine 146 isoperative to generate search results for users performing searchrequests.

Step 170 of the method of FIG. 3 includes generating or updating searchresults including updating the ranking of search result items based onthe authority rank. Thereby, in the system of FIG. 1, the client 102submits the search request to the search engine 106, the search requestbeing directed to a real time event causing a spike in user searchactivity. The search engine 106 is thereby operative to provide updatedsearch results included results adjusted based on the authority rankgenerated via the methodology of the steps of FIG. 3.

FIG. 4 illustrates another embodiment, whereby processing operationsprovides for improving search results relating to real time event andactivity spikes in searching, including processing information fromcontemporaneous sources. The method of FIG. 4 may be processed in thesystems of FIGS. 1 and 2 or any other suitable processing environment.

In this embodiment, a first step, step 180, is determining a pluralityof fresh web content for a search engine based on the active spike. Themethod of FIG. 4 may be predicated on the events of the method of FIG.3, including the determination of an activity spike. The method of FIG.4 provides additional processing operations to augment and/or supplementthe search results with new and/or additional search results, such asmay be found from contemporaneous sources, or at least via sources notyet captured using the web crawling techniques.

The determination of fresh web content may include direct web crawlingtechniques or searching contemporaneous feeds. For example, onetechnique may include searching a social network data feed of usersubmissions. The user submissions may be short messages, such as statusupdates or real-time messages, also colloquially known as a “tweet.” Anext step, step 182, is measuring real-time authority for the freshcontent using the machine-learning model. The measuring of the real-timeauthority for the fresh content may be performed using the machinelearning processing device 152 of FIG. 2 as described in further detailabove.

A next step, step 184, is adjusting a reliability factor for the freshweb content based on the measured real-time authority. The adjustment ofthe reliability factor includes utilizing the authority information fromstep 182. If the authority information indicates a high degree oftrustworthiness, the reliability factor can be improved and if theauthority information indicates a low degree of trustworthiness, thereliability factor can be lowered.

In this embodiment, a next step, step 186, is adjusting the reliabilityfactor based on the real-time authority instead of either a link fluxcalculation or a page rank adjustment. Similar to step 184, thereliability factor is adjusted, but in step 186, there is a reduction infactors used for this calculation. The methodology of FIG. 4 does notrequire the steps to be in sequential order and various embodiments caninclude step 186 instead of step 184 or vice versa. Step 186 furthermodifies step 184 by eliminating the adjustment factors of the link fluxcalculation and the page rank adjustment.

A next step, step 188, is determining an additional processing capacityin the search engine caused by the activity spike. In this embodiment, aprocessing operation examines the processing load search engine 106 ofFIG. 1 to account for this activity spike and the processing ofreal-time authority ranks as described above. This determination may beperformed using any suitable processing technique, such as monitoringprocessing load allocations to search operations on a search processingenvironment and measuring the delta between before the activity spikeand during the activity spike.

In the embodiment including step 188, an additional step, step 190, isallocating available processing capacity of the search engineproportional to the additional processing capacity. Therefore, in thisembodiment, the search engine provides for processing capacity asneeded, without seeking to lose or otherwise compromise processingoperations relating to the non-activity spike information. It isrecognized that just because there is a spike in user activity, there isstill a need to maintain standard search engine operations, therefore bythe allocation of step 190, attempts are made to maintain the searchengine but also efficiently and effectively provide real-time sourceinformation whereby there is a real-time authority ranking for thisinformation.

It is understood that search engines provides effective solutions tostandard searching operations, but based on the crawling data catalogingnature of these systems, problems can arise in real time activities.Based on the detection of the activity spike and machine-learningprocessing, the present method and system provides not only timesensitive search results, but also performs machine-learning authorityrank to improve the accuracy and benefit of the search results. Theauthority ranking allows for presentation of users with highest qualityresults in primary result positions, including account forcontemporaneous sources as described above.

FIGS. 1 through 4 are conceptual illustrations allowing for anexplanation of the present invention. It should be understood thatvarious aspects of the embodiments of the present invention could beimplemented in hardware, firmware, software, or combinations thereof. Insuch embodiments, the various components and/or steps would beimplemented in hardware, firmware, and/or software to perform thefunctions of the present invention. That is, the same piece of hardware,firmware, or module of software could perform one or more of theillustrated blocks (e.g., components or steps).

In software implementations, computer software (e.g., programs or otherinstructions) and/or data is stored on a machine readable medium as partof a computer program product, and is loaded into a computer system orother device or machine via a removable storage drive, hard drive, orcommunications interface. Computer programs (also called computercontrol logic or computer readable program code) are stored in a mainand/or secondary memory, and executed by one or more processors(controllers, or the like) to cause the one or more processors toperform the functions of the invention as described herein. In thisdocument, the terms “machine readable medium,” “computer program medium”and “computer usable medium” are used to generally refer to media suchas a random access memory (RAM); a read only memory (ROM); a removablestorage unit (e.g., a magnetic or optical disc, flash memory device, orthe like); a hard disk; or the like.

Notably, the figures and examples above are not meant to limit the scopeof the present invention to a single embodiment, as other embodimentsare possible by way of interchange of some or all of the described orillustrated elements. Moreover, where certain elements of the presentinvention can be partially or fully implemented using known components,only those portions of such known components that are necessary for anunderstanding of the present invention are described, and detaileddescriptions of other portions of such known components are omitted soas not to obscure the invention. In the present specification, anembodiment showing a singular component should not necessarily belimited to other embodiments including a plurality of the samecomponent, and vice-versa, unless explicitly stated otherwise herein.Moreover, applicants do not intend for any term in the specification orclaims to be ascribed an uncommon or special meaning unless explicitlyset forth as such. Further, the present invention encompasses presentand future known equivalents to the known components referred to hereinby way of illustration.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the relevant art(s) (including thecontents of the documents cited and incorporated by reference herein),readily modify and/or adapt for various applications such specificembodiments, without undue experimentation, without departing from thegeneral concept of the present invention. Such adaptations andmodifications are therefore intended to be within the meaning and rangeof equivalents of the disclosed embodiments, based on the teaching andguidance presented herein. It is to be understood that the phraseologyor terminology herein is for the purpose of description and not oflimitation, such that the terminology or phraseology of the presentspecification is to be interpreted by the skilled artisan in light ofthe teachings and guidance presented herein, in combination with theknowledge of one skilled in the relevant art(s).

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It would be apparent to one skilled in therelevant art(s) that various changes in form and detail could be madetherein without departing from the spirit and scope of the invention.Thus, the present invention should not be limited by any of theabove-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

What is claimed:
 1. A method, implemented one or more machines having atleast one processor, memory, and a communication platform connected to anetwork for allocating processing capacity of a search engine, themethod comprising: determining a plurality of content for a searchengine based on user search activity satisfying a condition; measuring areal-time authority for the plurality of content using amachine-learning model; adjusting a reliability factor for the pluralityof content; determining additional processing capacity of the searchengine as a result of the user search activity satisfying the condition;and allocating available processing capacity to the search engine basedon the additional processing capacity.
 2. The method of claim 1, whereinthe user search activity satisfying the condition comprises: determiningthat, during a period of time, user search request activity included anincreased amount of that one or more search terms within one or moresearch requests, the one or more search terms being associated with anevent.
 3. The method of claim 1, wherein the plurality of contentcomprises fresh web content, the method further comprises: searching oneor more contemporaneous data feeds; and identifying the plurality ofcontent within the one or more contemporaneous data feeds.
 4. The methodof claim 1, wherein measuring the real-time authority comprises:generating a rank for a source authority by evaluating the sourceauthority of a source associated with each of the plurality of content.5. The method of claim 1, wherein adjusting the reliability factorcomprises: utilizing the real-time authority to determine whether eachof the plurality of content indicates a high degree of trustworthinessor a low degree of trustworthiness, wherein: the reliability factor isincreased for content that indicates the high degree of trustworthiness,and the reliability factor is decreased for content that indicating thelow degree of trustworthiness.
 6. The method of claim 1, whereinadjusting the reliability factor comprises at least one of: adjustingthe reliability factor based on the real-time authority; and adjustingthe reliability factor by eliminating adjustment factors of link fluxcalculation and page rank adjustment.
 7. The method of claim 1, whereindetermining the additional processing capacity of the search enginecomprises: monitoring processing load allocations by measuring adifference between search operations of the search engine prior to theuser search activity satisfying the condition being detected and duringthe user search activity satisfying the condition.
 8. The method ofclaim 1, wherein allocating the available processing capacity to thesearch engine comprises: allocating the available processing capacity tothe search engine such the available processing capacity of the searchengine is proportional to the additional processing capacity of thesearch engine.
 9. A system for allocating processing capacity of asearch engine, comprising: a computer readable medium comprisinginstructions stored thereon; and a processing device that, in responseto executing the instructions, is operative to: determine a plurality ofcontent for a search engine based on user search activity satisfying thecondition; measure a real-time authority for the plurality of contentusing a machine-learning model; adjust a reliability factor for theplurality of content; determine additional processing capacity of thesearch engine as a result of the user search activity satisfying thecondition; and allocate available processing capacity to the searchengine based on the additional processing capacity.
 10. The system ofclaim 9, wherein the processing device, in response to executing theinstructions corresponding to the user search activity satisfying thecondition, is further operative to: determine that, during a period oftime, user search request activity included an increased amount of thatone or more search terms within one or more search requests, the one ormore search terms being associated with an event.
 11. The system ofclaim 9, wherein the plurality of content comprises fresh web content,the processing device, in response to executing the instructions, isfurther operative to: search one or more contemporaneous data feeds; andidentify the plurality of content within the one or more contemporaneousdata feeds.
 12. The system of claim 9, wherein the processing device, inresponse to executing the instructions corresponding to the real-timeauthority being measured, is operative to: generate a rank for a sourceauthority by evaluating the source authority of a source associated witheach of the plurality of content.
 13. The system of claim 9, wherein theprocessing device, in response to executing the instructionscorresponding to the reliability factor being adjusted, is operative to:utilize the real-time authority to determine whether each of theplurality of content indicates a high degree of trustworthiness or a lowdegree of trustworthiness, wherein: the reliability factor is increasedfor content that indicates the high degree of trustworthiness, and thereliability factor is decreased for content that indicating the lowdegree of trustworthiness.
 14. The system of claim 9, wherein theprocessing device, in response to executing the instructionscorresponding to the reliability factor being adjusted, is operative toat least one of: adjust the reliability factor based on the real-timeauthority; and adjust the reliability factor by eliminating adjustmentfactors of link flux calculation and page rank adjustment.
 15. Thesystem of claim 9, wherein the processing device, in response toexecuting the instructions corresponding to the additional processingcapacity of the search engine being determined, is operative to: monitorprocessing load allocations by measuring a difference between searchoperations of the search engine prior to the user search activitysatisfying the condition being detected and during the user searchactivity satisfying the condition.
 16. The system of claim 9, whereinthe processing device, in response to executing the instructionscorresponding to the available processing capacity to the search enginebeing allocated, is operative to: allocate the available processingcapacity to the search engine such the available processing capacity ofthe search engine is proportional to the additional processing capacityof the search engine.
 17. A non-transitory computer readable mediumcomprising instructions that, when executing by at least one processor,cause a device to: determine a plurality of content for a search enginebased on user search activity satisfying a condition; measure areal-time authority for the plurality of content using amachine-learning model; adjust a reliability factor for the plurality ofcontent; determine additional processing capacity of the search engineas a result of the user search activity satisfying the condition; andallocate available processing capacity to the search engine based on theadditional processing capacity.
 18. The non-transitory computer readablemedium of claim 17, wherein the instructions corresponding to adjustingthe reliability factor, when executed by the at least one processor,cause the device to: utilize the real-time authority to determinewhether each of the plurality of content indicates a high degree oftrustworthiness or a low degree of trustworthiness, wherein: thereliability factor is increased for content that indicates the highdegree of trustworthiness, and the reliability factor is decreased forcontent that indicating the low degree of trustworthiness.
 19. Thenon-transitory computer readable medium of claim 17, wherein theinstructions comprising the additional processing capacity of the searchengine being determined, when executed by the at least one processor,cause the device to: monitor processing load allocations by measuring adifference between search operations of the search engine prior to theuser search activity satisfying the condition being detected and duringthe user search activity satisfying the condition.
 20. Thenon-transitory computer readable medium of claim 17, wherein theinstructions comprising the available processing capacity to the searchengine being allocated, when executed by the at least one processor,cause the device to: allocate the available processing capacity to thesearch engine such the available processing capacity of the searchengine is proportional to the additional processing capacity of thesearch engine.